A Deep Analysis of Malaysian Covid-19 Cases in 2021

Initial Questions

TODO: Must have at least two questions. It is best to have different types of problems, ie one regression, and one classification

Objective

TODO: Analysis: Identify the questions, what is the objective/goal of processing this dataset? What answers are you interested to find through this dataset.
TODO: Determine the details about the dataset (eg. title, year, the purpose of dataset, dimension content, structure, summary) by exploring the raw data.
TODO: Short introduction with objective of the project.

Data Cleaning and Preprocessing

TODO: Which section of the data do you need to tidy?
TODO: Prepare data for analysis by correcting the variables and contents of the data.
TODO: Putting it all together as a new cleaned/processed dataset: For this task, you are also encouraged to explore any cleaning packages in R other than those learned in the course (diplyr, tidyr, lubridate, etc).

Step 1 : Import libraries
Step 2 : Import dataset
Step 3 : Handle missing/duplicate
Step 4 : Preprocessing

Import libraries

# if (!require('dplyr'))
#  install.packages('dplyr', repos='https://cran.asia/');
if (!require('kableExtra'))
  install.packages('kableExtra', repos='https://cran.asia/');
# if (!require('lubridate'))
#   install.packages('lubridate', repos='https://cran.asia/');
if (!require('plotly'))
  install.packages('plotly', repos='https://cran.asia/');
if (!require('plyr'))
  install.packages('plyr', repos='https://cran.asia/');
if (!require('raster'))
  install.packages('raster', repos='https://cran.asia/');
if (!require('scales'))
  install.packages('scales', repos='https://cran.asia/');
# if (!require('tidyquant'))
#   install.packages('tidyquant', repos='https://cran.asia/');
# if (!require('tidyr'))
#   install.packages('tidyr', repos='https://cran.asia/');


# library(dplyr)
library(kableExtra)
# library(lubridate)
library(plotly)
library(plyr)
library(raster)
library(scales)
# library(tidyquant)
# library('tidyr')

Import dataset

# covid_malaysia_endpoint <- 'https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/cases_malasia.csv'
# covid_state_endpoint <- 'https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/cases_state.csv'
covid_malaysia_endpoint <- 'cases_malaysia.csv'
covid_state_endpoint <- 'cases_state.csv'

df <- read.csv(covid_malaysia_endpoint, header=TRUE)
df_state <- read.csv(covid_state_endpoint, header=TRUE)

# Check the structure of the dataframe
str(df)

## 'data.frame':    708 obs. of  31 variables:
##  $ date                   : chr  "2020-01-25" "2020-01-26" "2020-01-27" "2020-01-28" ...
##  $ cases_new              : int  4 0 0 0 3 1 0 0 0 0 ...
##  $ cases_import           : int  4 0 0 0 3 1 0 0 0 0 ...
##  $ cases_recovered        : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_active           : int  4 4 4 4 7 8 8 8 8 8 ...
##  $ cases_cluster          : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_unvax            : int  4 0 0 0 3 1 0 0 0 0 ...
##  $ cases_pvax             : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_fvax             : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_boost            : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_child            : int  0 0 0 0 1 0 0 0 0 0 ...
##  $ cases_adolescent       : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_adult            : int  1 0 0 0 2 1 0 0 0 0 ...
##  $ cases_elderly          : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_0_4              : int  0 0 0 0 1 0 0 0 0 0 ...
##  $ cases_5_11             : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_12_17            : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_18_29            : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_30_39            : int  0 0 0 0 1 0 0 0 0 0 ...
##  $ cases_40_49            : int  1 0 0 0 0 1 0 0 0 0 ...
##  $ cases_50_59            : int  0 0 0 0 1 0 0 0 0 0 ...
##  $ cases_60_69            : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_70_79            : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_80               : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cluster_import         : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ cluster_religious      : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ cluster_community      : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ cluster_highRisk       : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ cluster_education      : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ cluster_detentionCentre: int  NA NA NA NA NA NA NA NA NA NA ...
##  $ cluster_workplace      : int  NA NA NA NA NA NA NA NA NA NA ...

str(df_state)

## 'data.frame':    11344 obs. of  25 variables:
##  $ date            : chr  "2020-01-25" "2020-01-25" "2020-01-25" "2020-01-25" ...
##  $ state           : chr  "Johor" "Kedah" "Kelantan" "Melaka" ...
##  $ cases_new       : int  4 0 0 0 0 0 0 0 0 0 ...
##  $ cases_import    : int  4 0 0 0 0 0 0 0 0 0 ...
##  $ cases_recovered : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_active    : int  4 0 0 0 0 0 0 0 0 0 ...
##  $ cases_cluster   : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_unvax     : int  4 0 0 0 0 0 0 0 0 0 ...
##  $ cases_pvax      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_fvax      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_boost     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_child     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_adolescent: int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_adult     : int  1 0 0 0 0 0 0 0 0 0 ...
##  $ cases_elderly   : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_0_4       : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_5_11      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_12_17     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_18_29     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_30_39     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_40_49     : int  1 0 0 0 0 0 0 0 0 0 ...
##  $ cases_50_59     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_60_69     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_70_79     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ cases_80        : int  0 0 0 0 0 0 0 0 0 0 ...

# Check the dimension of the dataframe
dim(df)

## [1] 708  31

dim(df_state)

## [1] 11344    25

# Check the first 6 rows
head(df) %>% kable('html') %>% kable_styling(font_size = 12)

date	cases_new	cases_import	cases_active	cases_unvax	cases_child	cases_adult	cases_0_4	cases_30_39	cases_40_49	cases_50_59	cluster_import	cluster_religious	cluster_community	cluster_highRisk	cluster_education	cluster_detentionCentre	cluster_workplace
2020-01-25	4	4	4	4	0	1	0	0	1	0	NA	NA	NA	NA	NA	NA	NA
2020-01-26	0	0	4	0	0	0	0	0	0	0	NA	NA	NA	NA	NA	NA	NA
2020-01-27	0	0	4	0	0	0	0	0	0	0	NA	NA	NA	NA	NA	NA	NA
2020-01-28	0	0	4	0	0	0	0	0	0	0	NA	NA	NA	NA	NA	NA	NA
2020-01-29	3	3	7	3	1	2	1	1	0	1	NA	NA	NA	NA	NA	NA	NA
2020-01-30	1	1	8	1	0	1	0	0	1	0	NA	NA	NA	NA	NA	NA	NA

head(df_state) %>% kable('html') %>% kable_styling(font_size = 12)

date	state	cases_new	cases_import	cases_active	cases_unvax	cases_adult	cases_40_49
2020-01-25	Johor	4	4	4	4	1	1
2020-01-25	Kedah	0	0	0	0	0	0
2020-01-25	Kelantan	0	0	0	0	0	0
2020-01-25	Melaka	0	0	0	0	0	0
2020-01-25	Negeri Sembilan	0	0	0	0	0	0
2020-01-25	Pahang	0	0	0	0	0	0

# Examine the statistics data
summary(df) %>% kable('html') %>% kable_styling(font_size = 12)

date	cases_new	cases_import	cases_recovered	cases_active	cases_cluster	cases_unvax	cases_pvax	cases_fvax	cases_boost	cases_child	cases_adolescent	cases_adult	cases_elderly	cases_0_4	cases_5_11	cases_12_17	cases_18_29	cases_30_39	cases_40_49	cases_50_59	cases_60_69	cases_70_79	cases_80	cluster_import	cluster_religious	cluster_community	cluster_highRisk	cluster_education	cluster_detentionCentre	cluster_workplace
Length:708	Min. : 0.0	Min. : 0.00	Min. : 0	Min. : 1	Min. : 0.0	Min. : 0.0	Min. : 0	Min. : 0.0	Min. : 0.00	Min. : 0.0	Min. : 0.0	Min. : 0.00	Min. : 0.0	Min. : 0.0	Min. : 0.0	Min. : 0.0	Min. : 0.0	Min. : 0.0	Min. : 0.0	Min. : 0.0	Min. : 0.0	Min. : 0.00	Min. : 0	Min. : 0.0000	Min. : 0.00	Min. : 0.0	Min. : 0.0	Min. : 0.00	Min. : 0.00	Min. : 14.0
Class :character	1st Qu.: 53.5	1st Qu.: 3.00	1st Qu.: 51	1st Qu.: 1212	1st Qu.: 17.0	1st Qu.: 53.5	1st Qu.: 0	1st Qu.: 0.0	1st Qu.: 0.00	1st Qu.: 2.0	1st Qu.: 3.0	1st Qu.: 38.75	1st Qu.: 4.0	1st Qu.: 1.0	1st Qu.: 1.0	1st Qu.: 3.0	1st Qu.: 16.0	1st Qu.: 10.0	1st Qu.: 6.0	1st Qu.: 4.0	1st Qu.: 2.0	1st Qu.: 1.00	1st Qu.: 0	1st Qu.: 0.0000	1st Qu.: 0.00	1st Qu.: 43.0	1st Qu.: 4.0	1st Qu.: 2.25	1st Qu.: 2.00	1st Qu.: 172.5
Mode :character	Median : 1542.0	Median : 6.00	Median : 1303	Median : 15124	Median : 364.0	Median : 1205.5	Median : 0	Median : 0.0	Median : 0.00	Median : 122.0	Median : 68.0	Median : 1156.50	Median : 93.5	Median : 46.5	Median : 75.0	Median : 68.0	Median : 466.5	Median : 391.5	Median : 197.0	Median : 119.0	Median : 64.5	Median : 22.00	Median : 8	Median : 0.0000	Median : 4.00	Median :137.5	Median : 16.0	Median : 16.50	Median : 37.00	Median : 494.5
NA	Mean : 3900.4	Mean : 12.21	Mean : 3798	Mean : 45991	Mean : 693.7	Mean : 2389.9	Mean : 557	Mean : 942.0	Mean : 11.48	Mean : 522.4	Mean : 257.1	Mean : 2666.92	Mean : 348.9	Mean : 208.0	Mean : 314.4	Mean : 257.1	Mean :1006.1	Mean : 820.5	Mean : 494.7	Mean : 345.7	Mean : 223.6	Mean : 92.29	Mean : 33	Mean : 0.4727	Mean : 23.19	Mean :198.8	Mean : 27.3	Mean : 37.53	Mean : 60.47	Mean : 624.4
NA	3rd Qu.: 5299.5	3rd Qu.: 13.00	3rd Qu.: 5119	3rd Qu.: 63406	3rd Qu.:1088.8	3rd Qu.: 3322.8	3rd Qu.: 92	3rd Qu.: 290.2	3rd Qu.: 0.00	3rd Qu.: 744.8	3rd Qu.: 291.5	3rd Qu.: 3596.00	3rd Qu.: 554.5	3rd Qu.: 294.2	3rd Qu.: 450.8	3rd Qu.: 291.5	3rd Qu.:1243.5	3rd Qu.:1136.8	3rd Qu.: 670.2	3rd Qu.: 518.0	3rd Qu.: 364.0	3rd Qu.:149.00	3rd Qu.: 50	3rd Qu.: 0.0000	3rd Qu.: 16.00	3rd Qu.:300.2	3rd Qu.: 41.0	3rd Qu.: 40.00	3rd Qu.: 86.00	3rd Qu.:1049.5
NA	Max. :24599.0	Max. :366.00	Max. :24855	Max. :263850	Max. :3394.0	Max. :12684.0	Max. :7318	Max. :8448.0	Max. :305.00	Max. :3437.0	Max. :1820.0	Max. :16450.00	Max. :1986.0	Max. :1362.0	Max. :2091.0	Max. :1820.0	Max. :6374.0	Max. :4922.0	Max. :3132.0	Max. :2066.0	Max. :1231.0	Max. :581.00	Max. :210	Max. :54.0000	Max. :359.00	Max. :825.0	Max. :189.0	Max. :501.00	Max. :439.00	Max. :2338.0
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA’s :342	NA’s :342	NA’s :342	NA’s :342	NA’s :342	NA’s :342	NA’s :342

summary(df_state) %>% kable('html') %>% kable_styling(font_size = 12)

date	state	cases_new	cases_import	cases_recovered	cases_active	cases_cluster	cases_unvax	cases_pvax	cases_fvax	cases_boost	cases_child	cases_adolescent	cases_adult	cases_elderly	cases_0_4	cases_5_11	cases_12_17	cases_18_29	cases_30_39	cases_40_49	cases_50_59	cases_60_69	cases_70_79	cases_80
Length:11344	Length:11344	Min. : 0.0	Min. : 0.0000	Min. : 0.0	Min. : -2.0	Min. : 0.0	Min. : 0.0	Min. : 0.00	Min. : 0.00	Min. : 0.0000	Min. : 0.00	Min. : 0.00	Min. : 0.0	Min. : 0.00	Min. : 0	Min. : 0.00	Min. : 0.00	Min. : 0.00	Min. : 0.00	Min. : 0.00	Min. : 0.0	Min. : 0.00	Min. : 0.000	Min. : 0.000
Class :character	Class :character	1st Qu.: 0.0	1st Qu.: 0.0000	1st Qu.: 0.0	1st Qu.: 11.0	1st Qu.: 0.0	1st Qu.: 0.0	1st Qu.: 0.00	1st Qu.: 0.00	1st Qu.: 0.0000	1st Qu.: 0.00	1st Qu.: 0.00	1st Qu.: 0.0	1st Qu.: 0.00	1st Qu.: 0	1st Qu.: 0.00	1st Qu.: 0.00	1st Qu.: 0.00	1st Qu.: 0.00	1st Qu.: 0.00	1st Qu.: 0.0	1st Qu.: 0.00	1st Qu.: 0.000	1st Qu.: 0.000
Mode :character	Mode :character	Median : 19.0	Median : 0.0000	Median : 16.0	Median : 274.5	Median : 3.0	Median : 14.0	Median : 0.00	Median : 0.00	Median : 0.0000	Median : 1.00	Median : 1.00	Median : 13.0	Median : 1.00	Median : 0	Median : 1.00	Median : 1.00	Median : 4.00	Median : 4.00	Median : 2.00	Median : 2.0	Median : 1.00	Median : 0.000	Median : 0.000
NA	NA	Mean : 243.7	Mean : 0.7916	Mean : 237.3	Mean : 2874.0	Mean : 43.3	Mean : 149.2	Mean : 34.77	Mean : 58.97	Mean : 0.7377	Mean : 32.64	Mean : 16.06	Mean : 166.6	Mean : 21.81	Mean : 13	Mean : 19.64	Mean : 16.06	Mean : 62.85	Mean : 51.27	Mean : 30.91	Mean : 21.6	Mean : 13.97	Mean : 5.768	Mean : 2.063
NA	NA	3rd Qu.: 237.0	3rd Qu.: 0.0000	3rd Qu.: 215.2	3rd Qu.: 2532.5	3rd Qu.: 40.0	3rd Qu.: 134.0	3rd Qu.: 3.00	3rd Qu.: 8.00	3rd Qu.: 0.0000	3rd Qu.: 29.00	3rd Qu.: 12.00	3rd Qu.: 160.0	3rd Qu.: 22.25	3rd Qu.: 11	3rd Qu.: 17.00	3rd Qu.: 12.00	3rd Qu.: 59.00	3rd Qu.: 50.00	3rd Qu.: 29.00	3rd Qu.: 22.0	3rd Qu.: 15.00	3rd Qu.: 6.000	3rd Qu.: 2.000
NA	NA	Max. :8792.0	Max. :74.0000	Max. :8801.0	Max. :94137.0	Max. :1545.0	Max. :6112.0	Max. :3890.00	Max. :3610.00	Max. :85.0000	Max. :1002.00	Max. :527.00	Max. :6549.0	Max. :637.00	Max. :429	Max. :608.00	Max. :527.00	Max. :2524.00	Max. :2097.00	Max. :1265.00	Max. :699.0	Max. :449.00	Max. :157.000	Max. :62.000

Handle missing/duplicate values

# Check for the columns with missing values
colSums(is.na(df)) %>% kable('html') %>% kable_styling(font_size = 12)

	x
date	0
cases_new	0
cases_import	0
cases_recovered	0
cases_active	0
cases_cluster	0
cases_unvax	0
cases_pvax	0
cases_fvax	0
cases_boost	0
cases_child	0
cases_adolescent	0
cases_adult	0
cases_elderly	0
cases_0_4	0
cases_5_11	0
cases_12_17	0
cases_18_29	0
cases_30_39	0
cases_40_49	0
cases_50_59	0
cases_60_69	0
cases_70_79	0
cases_80	0
cluster_import	342
cluster_religious	342
cluster_community	342
cluster_highRisk	342
cluster_education	342
cluster_detentionCentre	342
cluster_workplace	342

colSums(is.na(df_state)) %>% kable('html') %>% kable_styling(font_size = 12)

	x
date	0
state	0
cases_new	0
cases_import	0
cases_recovered	0
cases_active	0
cases_cluster	0
cases_unvax	0
cases_pvax	0
cases_fvax	0
cases_boost	0
cases_child	0
cases_adolescent	0
cases_adult	0
cases_elderly	0
cases_0_4	0
cases_5_11	0
cases_12_17	0
cases_18_29	0
cases_30_39	0
cases_40_49	0
cases_50_59	0
cases_60_69	0
cases_70_79	0
cases_80	0

# Show first few rows of the missing values
head(df[rowSums(is.na(df)) > 0,]) %>% kable('html') %>% kable_styling(font_size = 12)

date	cases_new	cases_import	cases_active	cases_unvax	cases_child	cases_adult	cases_0_4	cases_30_39	cases_40_49	cases_50_59	cluster_import	cluster_religious	cluster_community	cluster_highRisk	cluster_education	cluster_detentionCentre	cluster_workplace
2020-01-25	4	4	4	4	0	1	0	0	1	0	NA	NA	NA	NA	NA	NA	NA
2020-01-26	0	0	4	0	0	0	0	0	0	0	NA	NA	NA	NA	NA	NA	NA
2020-01-27	0	0	4	0	0	0	0	0	0	0	NA	NA	NA	NA	NA	NA	NA
2020-01-28	0	0	4	0	0	0	0	0	0	0	NA	NA	NA	NA	NA	NA	NA
2020-01-29	3	3	7	3	1	2	1	1	0	1	NA	NA	NA	NA	NA	NA	NA
2020-01-30	1	1	8	1	0	1	0	0	1	0	NA	NA	NA	NA	NA	NA	NA

head(df[rowSums(is.na(df_state)) > 0,]) %>% kable('html') %>% kable_styling(font_size = 12)

date	cases_new	cases_import	cases_recovered	cases_active	cases_cluster	cases_unvax	cases_pvax	cases_fvax	cases_boost	cases_child	cases_adolescent	cases_adult	cases_elderly	cases_0_4	cases_5_11	cases_12_17	cases_18_29	cases_30_39	cases_40_49	cases_50_59	cases_60_69	cases_70_79	cases_80	cluster_import	cluster_religious	cluster_community	cluster_highRisk	cluster_education	cluster_detentionCentre	cluster_workplace

# The missing rows for df can be ignored as there are 2020 data. 2021 data contains more columns.
# There is no missing rows for df_state.

# Check for duplicate values
df[duplicated(df)]

## data frame with 0 columns and 708 rows

df[duplicated(df_state)]

## data frame with 0 columns and 708 rows

# There are no duplicated rows

Preprocessing

df$date <- as.Date(df$date, format='%Y-%m-%d')
df_state$date <- as.Date(df_state$date, format='%Y-%m-%d')

Exploratory Data Analysis

TODO: Results may include visualization, prediction, evaluation of models and discussion of output

A brief Look on the graph

fig <- plot_ly(df, type = 'scatter', mode = 'lines')%>%
  add_trace(x = ~date, y = ~cases_new, name = 'Daily New Cvoid Cases')%>%
  layout(showlegend = F)
options(warn = -1)

fig <- fig %>%
  layout(
         xaxis = list(zerolinecolor = '#ffff',
                      zerolinewidth = 2,
                      gridcolor = 'ffff'),
         yaxis = list(zerolinecolor = '#ffff',
                      zerolinewidth = 2,
                      gridcolor = 'ffff'),
         plot_bgcolor='#e5ecf6', width = 1200)


fig

Density Map

df_state <- df_state %>%
  mutate(date = as.Date(df_state$date, format = "%Y-%m-%d")) %>%
  filter(date == as.Date('2021-09-01')) %>%
  mutate(state = replace(state, state == "W.P. Kuala Lumpur", "Kuala Lumpur")) %>%
  mutate(state = replace(state, state == "W.P. Labuan", "Labuan")) %>%
  mutate(state = replace(state, state == "W.P. Putrajaya", "Putrajaya")) %>%
  arrange(state) %>%
  dplyr::rename(NAME_1 = state)
  
malaysia <- getData("GADM", country = "MYS", level = 1)
malaysia@data$id <- rownames(malaysia@data)
malaysia@data <- join(malaysia@data, df_state, by = "NAME_1")
malaysia_df <- fortify(malaysia)

## Regions defined for each Polygons

malaysia_df <- join(malaysia_df, malaysia@data, by = "id")

theme_opts <- list(theme(
  panel.grid.minor = element_blank(),
  panel.grid.major = element_blank(),
  panel.background = element_blank(),
  plot.background = element_blank(),
  axis.line = element_blank(),
  axis.text.x = element_blank(),
  axis.text.y = element_blank(),
  axis.ticks = element_blank(),
  axis.title.x = element_blank(),
  axis.title.y = element_blank(),
  plot.title = element_blank()
))

# https://garthtarr.github.io/meatR/ggplot_extensions.html
# https://rstudio-pubs-static.s3.amazonaws.com/160207_ebe47475bb7744429b9bd4c908e2dc45.html
ggplot() +
  geom_polygon(data = malaysia_df, aes(x = long, y = lat, group = group, fill = cases_new), color = "white", size = 0.25) +
  theme(aspect.ratio = 2/5) +
  scale_fill_distiller(name = "No. of New Cases", palette = "YlOrRd", direction=1, breaks = pretty_breaks(n = 5)) +
  labs(title = paste('Number of New Cases in Each State on', '2021-09-01'))

Machine Learning

TODO: Results may include visualization, prediction, evaluation of models and discussion of output

Conclusion

TODO: Conclusion

Presentation and Submission

TODO Report: Submission will be an R markdown published at Rpubs, and the link is to be submitted in spectrum. The R markdown may include the following:

Short introduction with objective of the project.
Explanation of all the processes involved in the project
Results may include visualization, prediction, evaluation of models and discussion of output
Conclusion

TODO: Only one member per group will submit the report.
TODO: Each group is required to prepare a 10 minute presentation with powerpoint.
TODO: Both group members must present their parts.

A Deep Analysis of Malaysian Covid-19 Cases in 2021

Chai Kau Yu, Davion (S2019226)
William Heng Chun Meng (S2005592)
Lim Mei Cee (17162537)
Kamal Suria Asri Raja (S2110703)
Hong Zi Shen (S2114600)

04/01/22

Initial Questions

Objective

Data Cleaning and Preprocessing

Import libraries

Import dataset

Handle missing/duplicate values

Preprocessing

Exploratory Data Analysis

A brief Look on the graph

Density Map

Machine Learning

Conclusion

Presentation and Submission

End of Report

A Deep Analysis of Malaysian Covid-19 Cases in 2021

Chai Kau Yu, Davion (S2019226) William Heng Chun Meng (S2005592) Lim Mei Cee (17162537) Kamal Suria Asri Raja (S2110703) Hong Zi Shen (S2114600)

04/01/22

Initial Questions

Objective

Data Cleaning and Preprocessing

Import libraries

Import dataset

Handle missing/duplicate values

Preprocessing

Exploratory Data Analysis

A brief Look on the graph

Density Map

Machine Learning

Conclusion

Presentation and Submission

End of Report

Chai Kau Yu, Davion (S2019226)
William Heng Chun Meng (S2005592)
Lim Mei Cee (17162537)
Kamal Suria Asri Raja (S2110703)
Hong Zi Shen (S2114600)